T20 World Cup 2024 Match Data Analysis
A Step-by-Step Guide: How to Perform Feature Engineering
In the world of data science and machine learning, the adage “garbage in, garbage out” holds particularly true.
The quality and relevance of the features you feed into your models can make or break their performance. This is where the art and science of feature engineering come into play.
In this article, we’ll explore how to perform feature engineering in any data analysis project using a cricket match dataset.
Understanding Feature Engineering
Simply put, if I have to define it, I would say it is the process of using domain knowledge and creativity to extract new features from raw data.
These new features aim to make your machine learning algorithms or data analysis work better or to improve the interpretability of the results.
It’s a crucial step in the “data science pipeline,” often making the difference between a good model and a great one.
Why is feature engineering important?
- First, well-crafted features help capture complex patterns in the data that raw…
